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单细胞刺激-反应基因表达轨迹揭示了单个巨细胞动态反应的刺激特异性

Katherine M Sheu¹, Aditya Pimplaskar¹, Alexander Hoffmann¹

¹Department of Microbiology, Immunology, and Molecular Genetics, and Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, 611 Charles E. Young Dr S, Los Angeles, CA 90093, USA.

|October 16, 2024

概括

这项研究引入了单细胞基因表达轨迹 (scGETs),以揭示巨细胞对免疫威胁的动态反应. 这些动态改善了刺激-反应特异性,并揭示了协调的基因表达,这对于理解巨细胞功能至关重要.

科学领域:

免疫学和分子生物学
单细胞基因组学和转录基因组学

背景情况:

巨细胞是关键的免疫细胞,通过改变基因表达来调节对病原体和组织损伤的反应.
当前的技术难以捕捉免疫反应期间个体巨细胞内动态的实时基因表达变化.

研究的目的:

开发和应用一种方法来重建单个小鼠巨细胞中的高分辨率,时间序列基因表达动态.
研究动态基因表达信息对巨细胞刺激-反应特异性 (SRS) 的贡献.
评估微环境因素,如极化细胞因子,如何影响巨细胞的反应动态.

主要方法:

由六种不同的免疫刺激刺激的小鼠巨细胞生成高分辨率的单细胞RNA测序 (scRNA-seq) 时间序列数据.
来自scRNA-seq数据的实时单细胞基因表达轨迹 (scGET) 的输入集.
分析了scGETs,以评估它们对刺激-反应特异性的贡献,并确定免疫反应基因之间的相关性.

主要成果:

来自scGETs的动态信息显著提高了对不同刺激的巨细胞反应的特异性.
scGETs揭示了免疫反应基因之间的协调表达模式,表明生物调节.
与单个时间点测量相比,测量响应动态提供了巨细胞极化状态的优越歧视,突出显示了微环境背景的影响.

结论:

动态基因表达信息对于准确表征巨细胞刺激-反应特异性至关重要.
scGETs提供了一种强大的方法,可以在单细胞水平上剖析复杂的免疫反应.
了解动态基因表达反应对于定义巨细胞功能状态及其在免疫中的作用至关重要.

关键词:

上下文依赖性上下文依赖性动力学动力学动力学基因表达轨迹基因表达轨迹可诱导的基因表达.信息理论信息理论机器学习是机器学习.巨细胞是一个巨细胞.一个单细胞的单细胞.刺激-反应特异性特异性轨迹组合合在一起的轨迹.

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